Load CData Connect Cloud data to Microsoft Fabric

CData Connect Cloud provides a REST API that allows users to query data, perform batch operations, and execute stored procedures across configured data sources. Everything needed to build a working CData Connect Cloud → Microsoft Fabric pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your CData Connect Cloud to Microsoft Fabric pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the CData Connect Cloud API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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CData Connect Cloud API at a glance

These values come from the CData Connect Cloud API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the CData Connect Cloud API?

Authentication is performed using the Authorization header with a Base64-encoded string of 'email

', where the email is your account identifier and the password is your generated Personal Access Token. Standard HTTP Basic authentication can be used where the client handles the encoding.

1. Get your credentials

To obtain credentials for the CData Connect Cloud REST API, follow these steps: 1. Log in to your CData Connect Cloud account. 2. Click on the Gear icon in the top-right corner to open the Settings page. 3. Navigate to the Access Tokens section. 4. Click Create PAT (Personal Access Token). 5. Provide a name for the token and click Create. 6. Copy the generated PAT immediately, as it will not be displayed again. This token serves as the password for API authentication.

2. Add them to .dlt/secrets.toml

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[sources.cdata_connect_cloud_source]
cdata_username = "your_email@example.com"
cdata_pat = "your_personal_access_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What CData Connect Cloud data can I load into Microsoft Fabric?

These are the CData Connect Cloud endpoints dlt can load into Microsoft Fabric:

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How do I load only new CData Connect Cloud records?

The CData Connect Cloud API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "tables", "endpoint": {
    "path": "tables",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated CData Connect Cloud pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/query and /api/odata from the CData Connect Cloud API into Microsoft Fabric:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def cdata_connect_cloud_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://cloud.cdata.com/api",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "tables", "endpoint": {"path": "tables"}},
            {"name": "query", "endpoint": {"path": "query", "data_selector": "rows"}}
        ],
    }
    yield from rest_api_resources(config)


def load_cdata_connect_cloud_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="cdata_connect_cloud_pipeline",
        destination="fabric",
        dataset_name="cdata_connect_cloud_data",
    )
    load_info = pipeline.run(cdata_connect_cloud_source())
    print(load_info)


if __name__ == "__main__":
    load_cdata_connect_cloud_to_fabric()

Run it with python cdata_connect_cloud_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query CData Connect Cloud data in Microsoft Fabric?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("cdata_connect_cloud_pipeline").dataset()
df = data.tables.df()
print(df.head())

SQL:

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SELECT * FROM cdata_connect_cloud_data.tables LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the CData Connect Cloud to Microsoft Fabric pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw CData Connect Cloud loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load CData Connect Cloud data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack